Patterns of activities of daily living (ADLs) can provide early indicators of changes for many diseases and health conditions. Radio frequency (RF) sensing is promising for its contactless and privacy-preserving properties. However, most RF-based human activity recognition (HAR) studies focus on short, well-segmented actions, and their applicability to continuous, longer-duration ADLs remains largely uncertain. Existing continuous activity detection methods are typically trained in a closed set with labeled boundaries, limiting their ability to handle unseen, unsegmented ADLs in real life. In this work, we address both challenges by introducing ADL-CLIP, a framework for RF-based continuous ADL detection in home environments without pre-known boundaries and generalizable to unseen activities. We observe that intra-ADL radio frames cluster closely in the representation space, while those across ADLs are more separated. Building on this insight, we formulate ADL segmentation as a label-free optimization problem that identifies the boundaries by minimizing intra-segment while maximizing across-segment distances. To reconcile the discrepancy between data-driven segmentation and human annotated boundaries, we alternate between refining segmentation boundaries in the representation space and fine-tuning the representation model combining both boundaries using CLIP-style contrastive learning, progressively aligning the two while preserving prior knowledge from language models. We evaluate ADL-CLIP on a dataset collected from 46 non-researcher participants performing 25 ADLs continuously with 16 UWB sensors in an instrumented one-bedroom, one-bathroom apartment. In an open-set setting where 5 ADLs are held out during training, ADL-CLIP achieves 76.6% accuracy on all 25 ADLs, outperforming temporal activity detection baselines by 26% and NLS-based HAR by 34%. To our knowledge, this is the first RF-based open-set, boundary-free ADL detection study in an instrumented home environment.

Vehicle-to-vehicle (V2V) based cooperative perception enhances autonomous driving by overcoming single-agent perception limitations such as occlusions, without relying on extensive infrastructure. However, most existing methods have two key limitations. They treat cooperative perception in isolation, with little consideration for downstream tasks such as planning, leading to poor coordination and inefficient planning decisions. They also assume perception model homogeneity across all vehicles, which can be impractical among vehicles from different manufacturers. To bridge such gaps, we propose Scout, an early-fusion framework for planning-oriented cooperative perception among vehicles of heterogeneous models. Specifically, we formalize a notion of$\Delta \theta$-Risk Increment Distribution (RID) to capture the distribution of the risk increment by incomplete perception to the current trajectory plan, and define a Priority Index (PI) metric for prioritizing cooperative perception on riskier regions. We develop algorithms to estimate$\Delta \theta$-RID and PI at run-time with theoretical bounds. Empirical results demonstrate that Scout surpasses state-of-the-art methods and strong baselines on challenging benchmarks, achieving higher success rates with only 3-10% of their communication volume.

A data collection infrastructure is vital for generating sufficient amounts and diversity of data necessary for developing algorithms in home-based health monitoring. However, the manageability—deployment and operation efforts—of such an infrastructure has long been overlooked. Even a small size of a dozen homes may incur enormous manual efforts on the research team. In this article, we present Proteus, an easily managed infrastructure designed to automate much of the work in deploying and operating such systems. We develop new components and combine with mature technologies to minimize the human efforts required. Proteus includes: 1) scalable, continuous deployment, operation, and update of devices with automatic bootstrapping; 2) automatic fault and error monitoring and recovery with watchdogs and LED feedback, and complementary edge and cloud storage backups; and 3) an easy-to-use data-agnostic pipeline for integrating new modalities. We demonstrate our system’s robustness through different sets of experiments: three sensor nodes (SNs) running for 24 days sending data (17.4 Mb/s aggregate rate), 10 SNs for 14 days (58 Mb/s aggregate rate), and 32 emulated sensors (419.2 Mb/s aggregate rate). All such experiments have data loss rates less than 1%. Further we reduce human efforts by 25-fold and code required for adding new data modality by 25-fold. We also share our experience and lessons learned during the design, development, and pilot deployment of Proteus. Our results show that Proteus is a promising solution for enabling research teams to effectively manage home-based health monitoring at small to medium sizes.

Quantum computing, utilizing the unique properties of quantum mechanics, has the potential to revolutionize various fields. However, current quantum processors face challenges in scaling the number of qubits, limiting their practical applications. In response, Distributed Quantum Computing (DQC) has emerged as a promising paradigm where multiple interconnected Quantum Processing Units (QPUs) collaborate to execute quantum circuits. In this paper, we focus on designing networks to interconnect QPUs for the implementation of DQC. We find that in real-world experiments and systems, the photon collection and coupling efficiency is low, leading to significant performance degradation in direct connection networks. To address this limitation, we propose a novel multistage switching network tailored for DQC, which has low system complexity and high entanglement generation rates. The proposed switching network comprises$\log _{2}(N)$stages and$N/2$binary switches at each stage, where N represents the number of QPUs. We prove that the proposed network is nonblocking and develop an efficient routing algorithm with a time complexity of$\mathcal {O}(N\log (N))$. Additionally, we show the success probability of entanglement generation in the proposed switching network. Extensive simulations demonstrate that our network significantly outperforms the highly efficient circuit-switching Beneš network and three direct connection networks.

Decentralized model training for on-road vehicles offers the potential to harness huge amounts of data at low costs. However, existing approaches usually depend on the existence of a coordinator, tight synchronization, or a connected cluster, all of which can be challenging or infeasible for fast-moving vehicles. In this work, we propose Learning by Chatting (LbChat), a fully decentralized and asynchronous model training approach leveraging coreset-sharing to eliminate the need for a coordinator, tight synchronization, or even a connected cluster. Different from conventional decentralized learning methods, a vehicle not only exchanges its local model but also a coreset, a condensed abstract of its local training data, with opportunistically encountered peers. A vehicle measures its model's performance on a peer's coreset, and a lower performance indicates more different data, thus a more “valuable” model from the peer. Such models are compressed less during exchange to maximize the aggregate gain from each encounter. Extensive evaluations on the driving decision-making task demonstrate that LbChat is strongly competitive with the central server or roadside infrastructure-based approaches (e.g., federated learning). Compared to recent fully decentralized vehicular learning benchmarks, LbChat out-performs them significantly by up to 20% higher driving success rate in the most challenging driving condition, demonstrating the power of insights gained from coresets on peer models' value.

Mobile edge computing has emerged as a prevalent computing paradigm to support applications that demand low latency and high computational capacity. Hardware reconfigurable accelerators exhibit high energy efficiency and low latency compared to general-purpose servers, making them ideal for integration into mobile edge computing systems. This paper investigates the problem of joint task offloading, access point selection, and resource allocation in heterogeneous edge environments for latency minimization. Given the heterogeneity of edge computing devices and the interdependence of the decisions required for offloading, access point selection, and resource allocation, it is challenging to optimize over them simultaneously. We decomposed the proposed problem into two disjoint subproblems and developed algorithms for each of them. The first subproblem is to jointly determine access point selection and communication resource allocation decisions, for which we have proposed an algorithm with a provable approximation ratio of$2.62/(1-8\lambda )$, where$\lambda$is a tunable parameter balancing the approximation ratio and time complexity. Additionally, we offer a faster variant of the algorithm with an approximation ratio of$(\sqrt{3}+1)^{2}$. The second subproblem is to determine offloading and computing resource allocation decisions jointly and is NP-hard, where we developed algorithms based on relaxation and rounding. We conducted comprehensive numerical simulations to evaluate the proposed algorithms, and the results demonstrated that our algorithms outperformed existing baselines and achieved near-optimal performance across various settings.

Heart rate, a commonly accessible health data from most wearables, carries rich information of a person’s well-being, yet remains of limited deep health applications, due to the lack of groundtruth of health events and their impact on heart rate patterns. Specifically, standard health analytics usually are designed based on well-modeled health conditions thus known data patterns and rich training data. To bridge the gap, we propose HeartInsightify, an exploratory framework that facilitates the process of deriving health-relevant measurable indicators from longitudinal heart rate data, without any of the above knowledge. HeartInsightify focuses on comparative and qualitative study, using model-free statistical methods such as conformal prediction, to study similarities, perform clustering and detect outliers, and build multi-resolutional data summaries, allowing human experts to efficiently examine and verify their health relevance. We conduct extensive experiments to evaluate HeartInsightify using individuals’ free-living heart rate data collected through Fitbit over 6 years. We illustrate the process of analyzing heart rate data for its health relevance and demonstrate the effectiveness of HeartInsightify. We envision that HeartInsightify lays the groundwork for personalized health analytics with continuous monitoring data from wearables.

Fully decentralized model training for on-road vehicles can leverage crowdsourced data while not depending on central servers, infrastructure or Internet coverage. However, under unreliable wireless communication and short contact duration, model sharing among peer vehicles may suffer severe losses thus fail frequently. To address these challenges, we propose “RoADTrain”, a route-assisted decentralized peer model training approach that carefully chooses vehicles with high chances of successful model sharing. It bounds the per round communication time yet retains model performance under vehicle mobility and unreliable communication. Based on shared route information, a connected cluster of vehicles can estimate and embed the link reliability and contact duration information into the communication topology. We decompose the topology into subgraphs supporting parallel communication, and identify a subset of them with the highest algebraic connectivity that can maximize the speed of the information flow in the cluster with high model sharing successes, thus accelerating model training in the cluster. We conduct extensive evaluation on driving decision making models using the popular CARLA simulator. RoADTrain achieves comparable driving success rates and 1.2–4.5× faster convergence than representative decentralized learning methods that always succeed in model sharing (e.g., SGP), and significantly outperforms other benchmarks that consider losses by 17–27% in the hardest driving conditions. These demonstrate that route sharing enables shrewd selection of vehicles for model sharing, thus better model performance and faster convergence against wireless losses and mobility.

Mobile edge computing is becoming one of the ubiquitous computing paradigms to support applications requiring low latency and high computing capability. FPGA-based reconfigurable accelerators have high energy efficiency and low latency compared to general-purpose servers. Therefore, it is natural to incorporate reconfigurable accelerators in mobile edge computing systems. This paper formulates and studies the problem of joint task offloading, access point selection, and resource allocation in heterogeneous edge environments for latency minimization. Due to the heterogeneity in edge computing devices and the coupling between offloading, access point selection, and resource allocation decisions, it is challenging to optimize over them simultaneously. We decomposed the proposed problem into two disjoint subproblems and developed algorithms for them. The first subproblem is to jointly determine offloading and computing resource allocation decisions and is NP-hard, where we developed an algorithm based on semidefinite relaxation. The second subproblem is to jointly determine access point selection and communication resource allocation decisions, where we proposed an algorithm with a provable approximation ratio of 2.62. We conducted extensive numerical simulations to evaluate the proposed algorithms. Results highlighted that the proposed algorithms outperformed baselines and were near-optimal over a wide range of settings.

The trading of social media data has attracted wide research interests over years. In particular, the trading for Web browsing histories, when being applied to targeted advertising, produces tremendous economic value for data consumers. However, the disclosure of entire browsing histories, even in form of anonymous data sets, poses a huge threat to user privacy. Although some existing solutions have investigated privacy-preserving outsourcing of social media data, unfortunately, they neglected the impact on the data consumer’s utility. In this article, we propose CEATSE, a correlated data trading framework for various kinds of private Web browsing histories. CEATSE first models the correlation among multiple dimensional features, and then generates the optimal feature clustering scheme. Combined with this scheme, CEATSE next incorporates a correlated data perturbation strategy on each feature cluster, in order to balance the privacy-utility tradeoff. It then quantifies each chosen data contributor’s privacy loss on optimal feature clusters. Through real-data-based experiments, our analysis and evaluation results demonstrate CEATSE indeed achieves user privacy protection, the data consumer’s accuracy requirement, and truthfulness, individual rationality as well as budget balance.

Innovative edge devices (e.g., smartphones, IoT devices) are becoming much more pervasive in our daily lives. With powerful sensing and computing capabilities, users can generate massive amounts of data. A new business model has emerged where data producers can sell their data to consumers directly to make money. However, how to protect the profit of the data producer from rogue consumers that may resell without authorization remains challenging. In this paper, we propose a smart-contract based protocol to protect the profit of the data producer while allowing consumers to resell the data legitimately. The protocol ensures the revenue is shared with the data producer over authorized reselling, and detects any unauthorized reselling. We also introduce a data relay process that can enhance data accessibility in wireless edge networks. We formulate a revenue sharing problem to maximize the profit of both the data producer and resellers/relayers. We formulate the problem into a two-stage Stackelberg game and determine a ratio to share the reselling revenue between the data producer and resellers/relayers. Extensive simulations show that with resellers and relayers, our mechanism can achieve up to 49.5 percent higher profit for the data producer and resellers/relayers.

The democratization of indoor tracking systems lays the groundwork for a wide spectrum of smart home applications. While prior work on RF-based device-free localization/tracking have shown preferable features and promising results, they heavily relied on well-calibrated sensor placements, which require hours of intensive manual setup and respective expertise, making it prohibitively expensive to scale deployments to wide range (e.g., tens or hundreds of real homes). We propose SCALING, a plug-and-play indoor tracking system, of which the key enabler is a self calibrating algorithm that estimates the distributed sensor locations through their distance measurements to a person walking a trajectory, a trivial effort without taxing layman users physically or cognitively. We have experimentally evaluated SCALING via real world testbeds and shown an 80-percentile tracking accuracy of 40.5 cm, only 1% degradation compared to the classical multilateration with known sensor locations (anchors), which costs hours of intensive calibrating effort.

Basic vital signs such as heart and respiratory rates (HR and RR) are essential bio-indicators. Their longitudinal in-home collection enables prediction and detection of disease onset and change, providing for earlier health intervention. In this article, we propose a robust, non-touch vital signs monitoring system using a pair of co-located Ultra-Wide Band (UWB) and depth sensors. By extensive manual examination, we identify four typical temporal and spectral signal patterns and their suitable vital sign estimators. We devise a probabilistic weighted framework (PWF) that quantifies evidence of these patterns to update the weighted combination of estimator output to track the vital signs robustly. We also design a “heatmap”-based signal quality detector to exclude the disturbed signal from inadvertent motions. To monitor multiple co-habiting subjects in-home, we build a two-branch long short-term memory (LSTM) neural network to distinguish between individuals and their activities, providing activity context crucial to disambiguating critical from normal vital sign variability. To achieve reliable context annotation, we carefully devise the feature set of the consecutive skeletal poses from the depth data, and develop a probabilistic tracking model to tackle non-line-of-sight (NLOS) cases. Our experimental results demonstrate the robustness and superior performance of the individual modules as well as the end-to-end system for passive and context-aware vital sign monitoring.

Edge computing is becoming pervasive in our daily lives with emerging smart devices and the development of communication technology. Resource-rich smart devices and high-density supportive networks make data transactions prevalent over edge environments. To ensure such transactions are unmodifiable and undeniable, blockchain technology is introduced into edge environments. In this paper, we propose a hybrid blockchain system to enhance the security for transactions and determine the incentive for miners in edge computing environments. We propose a Proof of Work (PoW) and Proof of Stake (PoS) hybrid consensus blockchain system utilizing the heterogeneity of devices to adapt to the characteristic of edge environments. We raise the incentive assignment problem for a fair incentive to PoW miners. We formulate the problem and propose an iterative and another heuristic algorithm to determine the incentive that the miner will receive for a new block. We further prove that the iterative algorithm can obtain global optimal results. Numerical simulation results show that our proposed algorithm can give a reasonable incentive to miners under different system parameters in edge blockchain systems.

Edge devices with sensing, storage, and communication resources are penetrating our daily lives. These resources make it possible for edge devices to conduct data transactions (e.g., micro-payments, micro-access control). The blockchain technology can be used to ensure transaction unmodifiable and undeniable. In this paper, we propose a blockchain system that adapts to the limitations of edge devices. The new blockchain system can fairly and efficiently allocate storage resources on edge devices, which makes it scalable. We find the optimal peer nodes for transaction data storage and propose a recent block storage allocation scheme for quick retrieval of missing blocks. We develop data migration algorithms to dynamically reallocate data and block storage to adapt topology changes in the network. The proposed blockchain system can also reach consensus with low energy consumption in edge devices with a new Proof of Stake mechanism. Extensive simulations show that our proposed blockchain system works efficiently in edge environments. On average, the new system uses 18.4 percent less time and consumes 87 percent less battery power when compared with traditional blockchain systems.

In IoT, what services from which nearby devices are available, must be discovered by a user's device (e.g., smartphone) before she can issue commands to access them. Service visibility scoping in large scale, heterogeneous enterprise environments has multiple unique features, e.g., proximity based interactions, differentiated visibility according to device natures and user attributes, frequent user churns thus revocation. They render existing solutions completely insufficient. We propose Argus, a distributed algorithm offering three-level, fine-grained visibility scoping in parallel: i) Level 1 public visibility where services are identically visible to everyone; ii) Level 2 differentiated visibility where service visibility depends on users’ non-sensitive attributes; iii) Level 3 covert visibility where service visibility depends on users’ sensitive attributes that are never explicitly disclosed. Extensive analysis and experiments show that: i) Argus is secure; ii) its Level 2 is 10x as scalable and computationally efficient as work using Attribute-based Encryption, Level 3 is 10x as efficient as work using Paring-based Cryptography; iii) it is fast and agile for satisfactory user experience, costing 0.25 s to discover 20 Level 1 devices, and 0.63 s for Level 2 or Level 3 devices.

User authentication on smartphones is the key to many applications, which must satisfy both security and convenience. We propose a novel user authentication systemEchoPrint, which leverages acoustics and vision for secure and convenient user authentication, without requiring any special hardware.EchoPrintactively emits almost inaudible acoustic signals from the earpiece speaker to “illuminate” the user's face and authenticates the user by the unique features extracted from the echoes bouncing off the 3D facial contour. To combat changes in phone-holding poses thus echoes, a convolutional neural network (CNN) is trained to extract reliable acoustic features, which are further combined with visual facial features extracted from state-of-the-art face recognition deep models to feed a binary support vector machine (SVM) classifier for final authentication. Because the echo features depend on 3D facial geometries,EchoPrintis not easily spoofed by images or videos like 2D visual face recognition systems. It needs only commodity hardware, thus avoiding the extra costs of special sensors in solutions like FaceID. Experiments with 62 volunteers and non-human objects such as images, photos, and sculptures show thatEchoPrintachieves 93.75 percent balanced accuracy and 93.50 percent F-score, while the average precision is 98.05 percent using acoustic features and basic facial landmarks. The precision is further improved to 99.96 percent with sophisticated visual features.

With the commoditization of private data, data trading in consideration of user privacy protection has become a fascinating research topic. The trading for private web browsing histories brings huge economic value to data consumers when leveraged by targeted advertising. And the online pricing of these private data further helps achieve more realistic data trading. In this paper, we study the trading and pricing of multiple correlated queries on private web browsing history data at the same time. We propose CTRADE, which is a novel online data CommodiTization fRamework for trAding multiple correlateD queriEs over private data. CTRADE first devises a modified matrix mechanism to perturb query answers. It especially quantifies privacy loss under the relaxation of classical differential privacy and a newly devised mechanism with relaxed matrix sensitivity, and further compensates data owners for their diverse privacy losses in a satisfying manner. CTRADE then proposes an ellipsoid-based query pricing mechanism according to a given linear market value model, which exploits the features of the ellipsoid to explore and exploit the close-optimal dynamic price at each round. In particular, the proposed mechanism produces a low cumulative regret, which is quadratic in the dimension of the feature vector and logarithmic in the number of total rounds. Through real-data based experiments, our analysis and evaluation results demonstrate that CTRADE balances total error and privacy preferences well within acceptable running time, indeed produces a convergent cumulative regret with more rounds, and also achieves all desired economic properties of budget balance, individual rationality, and truthfulness.

With the plethora of wireless devices in limited spaces running multiple WiFi standards (802.11 a/b/g/n/ac), gaining an understanding of network latency, loss, and medium utilization becomes extremely challenging. Microsecond timing fidelity with protocols is critical for network performance evaluation and design; such fine-grained timing offers insights that simulations cannot deliver (e.g., precise timing through hardware and software implementations). However, currently there is no suitable efficient, lightweight tool for such purposes. This paper introduces Aletheia, an open-source tool that enables users to select their interested attributes in WiFi frames, quantify and visualize microsecond granularity medium utilization using low-cost commodity edge devices for easy deployment. Aletheia uses selective attribute extraction to filter frame fields; it reduces data storage and CPU overhead by 1 and 2 orders of magnitude, respectively, compared to existing tools (e.g., Wireshark, tshark). It provides flexible tagging and visualization features to examine the medium and perform different analysis to understand protocol behavior under different environments. We use Aletheia to capture and analyze 120M frames in 24 hours at 4 locations to demonstrate its value in production and research network performance evaluation and troubleshooting. We find that WiFi management beacons can consume medium heavily (up to 40%); the common practice of categorizing networks based on environment types (e.g., office vs. home) is problematic, calling for a different evaluation methodology and new designs.

Edge computing guides the collaborative work of widely distributed nodes with different sensing, storage, and computing resources. For example, sensor nodes collect data and then store it in storage nodes so that computing nodes can access the data when needed. In this paper, we focus on the quality of service (QoS) in storage allocation in edge networks. We design a reputation mechanism for nodes in edge networks, which enables interactive nodes to evaluate the quality of service for reference. Each node publicly broadcasts a personal reputation list to evaluate all other nodes, and each node can calculate the global reputation of all nodes by aggregating personal reputations. We then propose a storage allocation algorithm that stores data to appropriate locations. The algorithm considers fairness, efficiency, and reliability which is derived from reputations. We build a novel Proof-of-Reputation (PoR) blockchain to support consensus on the reputation mechanism and storage allocation. The PoR blockchain ensures safety performance, saves computing resources, and avoids centralization. Extensive simulation results show our proposed algorithm is fair, efficient, and reliable. The results also show that in the presence of attackers, the success rate of honest nodes accessing data can reach 99.9%.

With mobile devices extensively used in daily life, there are ample opportunities to exchange sensing data through them, even without centralized management. In this paper, we design a peer based data exchanging model, where relay nodes move to certain locations to connect data providers and consumers to facilitate data delivery. Consumers are willing to pay for the data and these rewards are given to both relays and data providers. We first prove the NP-hardness of the problem on how to assign relay nodes to proper locations, and present a centralized optimal method with an approximation ratio. Then we define an autonomous compensation game for relays to make their individual decisions without any central authority. The sufficient and necessary condition for the existence of Nash equilibrium is derived, and an efficient reinforcement learning solver is designed to find the exact forms of equilibria. We analyze and compare this distributed game to the centralized social optimal solution, showing that the game incurs small bounded social costs, and is efficient under various network sizes, number of providers, number of consumers and device mobility.

Scalable, fine-grained access control for Internet-of-Things is needed in enterprise environments, where tens of thousands of users need to access smart objects which have a similar or larger order of magnitude. Existing solutions offer all-or-nothing access, or require all access to go through a cloud backend, greatly impeding access granularity, robustness and scale. In this paper, we propose Heracles, an IoT access control system which achieves robust, fine-grained access control and responsive execution at enterprise scale. Heracles adopts a capability-based approach using secure, unforgeable tokens that describe the authorizations of users, to either individuals or collections of objects in single or bulk operations. It has a 3-tier architecture to provide centralized policy and distributed execution desired in enterprise environments. Extensive analysis and performance evaluation on a testbed prove that Heracles achieves fine-grained access control and responsive execution at enterprise scale. Compared with systems using access control list, Heracles eliminates or reduces by 10x-100x the updating overhead under frequent changes of subject memberships and policies. Besides, Heracles achieves responsive execution: it takes 0.57 second to access 18 objects which are scattered 1-9 hops away, and execution on a 1-hop or 2-hop object needs only 0.07 or 0.13 second respectively.

In IoT it is common that before a command can execute on a smart object, certain preconditions (on possibly other objects) should be met first to ensure safety or efficiency. Existing work has realized automatic precondition execution: when a user issues a command, her device automatically finds out all the precondition commands, and executes them in the correct order. However, security issues have not been considered: it assumes that a user device honestly follows the order it has been told to send commands to objects, and objects trust users thus do not check whether the preconditions are indeed met. In this paper we propose two strategies to enforce precondition execution order: 1) Snowball relying on signed declarations from precondition objects; 2) Onion using disposable access tokens encrypted by a trustworthy server. Our extensive analysis and experiments on a 20-node testbed show that both strategies are secure and reliable. Snowball has higher availability while Onion is more efficient and responsive: Onion uses 1.6/2.1 s to access 20 one-hop/multi-hop objects, 62%/54% of Snowball's time.

Peer communication among edge devices (e.g., mobiles, vehicles, IoT and drones) is frequently data-centric: most important is obtaining data of desired content from suitable nodes; who generated or transmitted the data matters much less. Typical cases are robust one-to-many data sharing: e.g., a vehicle sending weather, road, position and speed data streams to nearby cars continuously. Unfortunately, existing address-based wireless communication is ill-suited for such purposes. We propose V-MAC, a novel data-centric radio that provides a pub/sub abstraction to replace the point-to-point abstraction in existing radios. It filters frames by data names instead of MAC addresses, thus eliminating complexities and latencies in neighbor discovery and group maintenance in existing radios. V-MAC supports robust, scalable and high rate multicast with consistently low losses across receivers of varying reception qualities. Experiments using a Raspberry Pi and a commodity WiFi dongle based prototype show that V-MAC reduces loss rate from WiFi broadcast's 50-90% to 1-3% for up to 15 stationary receivers, 4-5 moving people, and miniature and real vehicles. It cuts down filtering latency from 20μs in WiFi to 10μ s for up to 2 million data names, and improves cross stack latency 60-100× for TX/RX paths. We have ported V-MAC to 4 major WiFi chipsets (including 802.11 a/b/g/n/ac radios), 6 different platforms (Android, embedded and FPGA systems), 7 Linux kernel versions, and validated up to 900Mbps multicast data rate and interoperation with regular WiFi. We will release V-MAC as a mature, reusable asset for edge computing research.

With the commoditization of private data, data trading in consideration of user privacy protection has become a fascinating research topic. The trading for private web browsing histories brings huge economic value to data consumers when leveraged by targeted advertising. In this paper, we study the trading of multiple correlated queries on private web browsing history data. We propose TERBE, which is a novel trading framework for correlaTed quEries based on pRivate web Browsing historiEs. TERBE first devises a modified matrix mechanism to perturb query answers. It then quantifies privacy loss under the relaxation of classical differential privacy and a newly devised mechanism with relaxed matrix sensitivity, and further compensates data owners for their diverse privacy losses in a satisfying manner. Through real-data based experiments, our analysis and evaluation results demonstrate that TERBE balances total error and privacy preferences well within acceptable running time, and also achieves all desired economic properties of budget balance, individual rationality, and truthfulness.

Innovative edge devices (e.g., smartphones, IoT devices) are becoming much more pervasive in our daily lives. With powerful sensing and computing capabilities, users can generate massive amounts of data. A new business model has emerged where data producers can sell their data to consumers directly to make money. However, how to protect the profit of the data producer from rogue consumers that may resell without authorization remains challenging. In this paper, we propose a smart-contract based protocol to protect the profit of the data producer while allowing consumers to resell the data legitimately. The protocol ensures the revenue is shared with the data producer over authorized reselling, and detects any unauthorized reselling. We formulate a fair revenue sharing problem to maximize the profit of both the data producer and resellers. We formulate the problem into a two-stage Stackelberg game and determine a ratio to share the reselling revenue between the data producer and resellers. Extensive simulations show that with resellers, our mechanism can achieve higher profit for the data producer and resellers.

In IoT, what services from which nearby devices are available, must be discovered by a user's device (e.g., smartphone) before she can issue commands to access them. Service visibility scoping in large scale, heterogeneous enterprise environments has multiple unique features, e.g., proximity based interactions, differentiated visibility according to device natures and user attributes, frequent user churns thus revocation. They render existing solutions completely insufficient. We propose Argus, a distributed algorithm offering three-level, fine-grained visibility scoping in parallel: i) Level 1 public visibility where services are identically visible to everyone; ii) Level 2 differentiated visibility where service visibility depends on users' non-sensitive attributes; iii) Level 3 covert visibility where service visibility depends on users' sensitive attributes that are never explicitly disclosed. Extensive analysis and experiments show that: i) Argus is secure; ii) its Level 2 is 10x as scalable and computationally efficient as work using Attribute-based Encryption, Level 3 is 10x as efficient as work using Paring-based Cryptography; iii) it is fast and agile for satisfactory user experience, costing 0.25 s to discover 20 Level 1 devices, and 0.63 s for Level 2 or Level 3 devices.

Edge computing is becoming pervasive in our daily lives with emerging smart devices and the development of communication technology. Resource-rich smart devices and high-density supportive networks make data transactions prevalent over edge environments. To ensure such transactions are unmodifiable and undeniable, blockchain technology is introduced into edge environments. In this paper, we propose a hybrid blockchain system in edge environments to enhance the security for transactions and determine the incentive for miners. We propose a Proof of Work (PoW) and Proof of Stake (PoS) hybrid consensus blockchain system utilizing the heterogeneity of devices to adapt to the characteristic of edge environments. We raise the incentive assignment problem that gives the corresponding PoW miner when a new block generates. We further formulate it into a two-stage Stackelberg game. We propose an algorithm and prove that it can obtain the global optimal results for the incentive that the miner will receive for a new block. Numerical simulation results show that our proposed algorithm can give reasonable incentive to miners under different system parameters in edge blockchain systems.

Edge devices with sensing, storage, and communication resources (e.g., smartphones, tablets, connected vehicles, and IoT nodes) are increasingly penetrating our daily lives. Many novel applications can be created through sharing data among nearby peer edge devices. In such applications, caching data at some edge devices can greatly improve data availability, retrieval robustness, and delivery latency. In this paper, we study the unique problem of caching fairness in edge computing environments. Due to the heterogeneity of peer edge devices, load balance is a critical issue that affects the fairness in caching. We propose fairness metrics to characterize this issue and formulate the caching fairness problem as an integer linear programming problem, which is shown as the summation of multiple Connected Facility Location (ConFL) problems. We provide an approximation algorithm by leveraging an existing ConFL approximation algorithm, and prove that it preserves a 6.55 approximation ratio. We further develop a distributed algorithm where devices exchange data reachability information and identify popular candidates as caching nodes. Finally, we update the fairness metric and apply it to algorithms for making continuous caching decisions overtime. Our extensive evaluation results show that compared with existing caching algorithms for wireless networks, our proposed algorithms significantly improve the data caching fairness while keeping the contention induced latency comparable to the best existing algorithms.

The lack of digital floor plans is a huge obstacle to pervasive indoor location based services (LBS). Recent floor plan construction work crowdsources mobile sensing data from smartphone users for scalability. However, they incur long time (e.g., weeks or months) and tremendous efforts in data collection. In this paper, we propose BatMapper, which explores a previously untapped sensing modality-acoustics-forfast, fine grained, and low cost floor plan construction. We design sound signals suitable for heterogeneous microphones on commodity smartphones, and acoustic signal processing techniques to produce accurate distance measurements to nearby objects. We further develop robust probabilistic echo-object association, recursive outlier removal, and probabilistic resampling algorithms to identify the correspondence between distances and objects, thus the geometry of corridors and rooms. We compensate minute hand sway movements to identify small surface recessions, thus detecting doors automatically. Experiments in real buildings show BatMapperachieves 1 - 2 cm distance accuracy in ranges up around 4 m; a 2~3 minute walk generates fine grained corridor shapes, detects doors at 92 percent precision and 1~2 mlocation error at 90-percentile; and tens of seconds of measurement gestures produce room geometry with errors <; 0:3 m at 80-percentile, at 1 - 2 orders of magnitude less data amounts and user efforts.

Internet of Things in enterprise environments features large numbers of devices deployed in rooms, floors of possibly multiple buildings. Delivering user commands to control devices nearby and multiple hops away requires efficient and scalable routing in such environments. Existing work in ad-hoc, sensor or IoT network routing lacks good human accessibility and scalability. In this paper, we propose a peer-based protocol GraphiteRouting. All devices carry human-readable hierarchical string names for easy reference. It leverages devices' installation hierarchy for scalable hierarchical routing: most devices maintain only a few to dozens of routing entries for same-room devices, and a fraction of devices act as gateways for traffic from/to other rooms, floors or buildings. Also, it leverages users' operation patterns to less optimize infrequently used routes. Extensive analysis and performance evaluation on a 20-node testbed prove that GraphiteRouting is scalable: it has routing tables 10x- -1000x smaller than those in peer-based flat routing; also, upon device joining/leaving, its routing entries converge in less than 5 s, and forwarding a user command over 8 hops costs less than 0.3 s.

User authentication on smartphones is the key to many applications, which must satisfy both security and convenience. We propose a multi-modal face authentication system, which pushes the limit of state-of-the-art image based face recognition solutions by incorporating a new dimension of sensing modality - acoustics. It actively emits almost inaudible acoustic signals from the earpiece speaker to "illuminate" the user's face and extracts features from the echoes using a customized convolutional neural network, which are fused with sophisticated visual features extracted from state-of-the-art face recognition models, for secure face authentication. Because the echo features depend on 3D facial geometries and material, our multi-modal design is not easily spoofed by images or videos like image based face recognition systems. It does not require any special sensors thus eliminating the extra costs in solutions like FaceID. Experiments show that our design achieves comparable face recognition performance to the state-of-the-art image based face authentication, while able to block image/video spoofing.

Edge devices with sensing, storage, and communication resources are penetrating our daily lives. These resources make it possible for edge devices to conduct data transactions (e.g., micro-payments, micro-access control). The blockchain technology can be used to ensure transaction unmodifiable and undeniable. In this paper, we propose a blockchain system that adapts to the limitations of edge devices. The new blockchain system can fairly and efficiently allocate storage resources on edge devices, which makes it scalable. We find the optimal peer nodes for transaction data storage in the blockchain, and propose a recent block storage allocation scheme for quick retrieval of missing blocks. The proposed blockchain system can also reach mining consensus with low energy consumption in edge devices with a new Proof of Stake mechanism. Extensive simulations show that our proposed blockchain system works efficiently in edge environments. On average, the new system uses 15% less time and consumes 64% less battery power when compared with traditional blockchain systems.

The trading of social media data has attracted wide research interests over years. Especially the trading for web browsing histories probably produces tremendous economic value for data consumers when being applied to targeted advertising. However, the disclosure of entire browsing histories, even in form of anonymous datasets poses a huge threat to user privacy. Although some existing solutions have investigated privacy-preserving outsourcing of social media data, unfortunately, they neglected the impact on the data consumer's utility. In this paper, we propose PEATSE, a new Privacy-prEserving dAta Trading framework for web browSing historiEs. It takes users' diverse privacy preferences and the utility of their web browsing histories into consideration. PEATSE perturbs users' detailed browsing times on released browsing records to protect user privacy, while balancing the privacy-utility tradeoff. Through real-data based experiments, our analysis and evaluation results demonstrate PEATSE indeed achieves user privacy protection, the data consumer's accuracy requirement, and truthfulness, individual rationality as well as budget balance.

As metropolises develop, air pollution has become a serious problem, especially in developing countries like China. Many governments and researchers have devoted themselves to tackling and solving this problem. With the proliferation of smartphones, mobile crowdsensing is becoming a promising paradigm for monitoring large-scale environmental phenomena. In a practical crowdsensing system, incentives should be provided to encourage the participation of rational smartphone users, because it incurs various costs on users to collect sensing data. However, monitoring fine-grained air pollution in a large urban area based on crowdsensing will lead to high payments, which makes designing an efficient incentive mechanism a challenging problem. Fortunately, compressive sensing (CS) has been proved as an effective technology to reduce the amount of collected data via exploiting the spatial correlations among sensing data. In this article, we employ CS in the air pollution monitoring application, in which only a sampled set of locations are selected to collect data and provide incentives to the participants, and air pollution concentrations in unselected locations are inferred via CS. We propose an active learning scheme, which iteratively selects valuable locations to collect sensing data. Moreover, an expectation maximization-based algorithm is designed to detect the contexts in which sensing data are collected, and an efficient incentive mechanism is provided to encourage users with low costs participating. Comprehensive simulations are conducted to demonstrate the performance of our proposed scheme.

A lack of floor plans is a fundamental obstacle to ubiquitous indoor location-based services. Recent work have made significant progress to accuracy, but they largely rely on slow crowdsensing that may take weeks or even months to collect enough data. In this paper, we propose Knitter that can generate accurate floor maps by a single random user’s one hour data collection efforts, and demonstrate how such maps can be used for indoor navigation. Knitter extracts high quality floor layout information from single images, calibrates user trajectories, and filters outliers. It uses a multi-hypothesis map fusion framework that updates landmark positions/orientations and accessible areas incrementally according to evidences from each measurement. Our experiments on three different large buildings (up to$140\times 50\;\mathrm{m}^2$) with 30+ users show that Knitter produces correct map topology, with landmark location errors of$3\sim 5\;\mathrm{m}$and orientation errors of$4\sim 6^\circ$, both at 90-percentile. Our results are comparable to the state-of-the-art at more than$20\times$speed up: data collection in each of the three buildings can finish in about one hour even by a novice user trained just a few minutes.

Scalable, fine-grained access control for Internet-of-Things is needed in enterprise environments, where thousands of subjects need to access possibly one to two orders of magnitude more objects. Existing solutions offer all-or-nothing access, or require all access to go through a cloud backend, greatly impeding access granularity, robustness and scale. In this paper, we propose Heracles, an IoT access control system that achieves robust, fine-grained access control at enterprise scale. Heracles adopts a capability-based approach using secure, unforgeable tokens that describe the authorizations of subjects, to either individual or collections of objects in single or bulk operations. It has a 3-tier architecture to provide centralized policy and distributed execution desired in enterprise environments, and delegated operations for responsiveness of resource-constrained objects. Extensive security analysis and performance evaluation on a testbed prove that Heracles achieves robust, responsive, fine-Qrained access control in large scale enterprise environments.

Emerging innovative edge devices like drones, self-driving cars, phones/tablets and IoT nodes are revolutionizing our daily lives. Caching data among peer edge devices enables data sharing needed in many applications. In such applications, network scalability and node mobility bring many challenges. They change the topology and the resources in the network and make the network less robust. In this paper, we propose peer data caching strategies that consider the scale and mobility of these increasingly popular edge devices. We propose a grouping method creating a layered design to reduce the number of entities in each layer. We propose inter-group and intra-group optimization problems which proactively cache data onto best places to support robust and fast data access. We develop a 7-approximation algorithm for inter-group optimization and use uncapacitated facility location problems to solve intra-group optimization. We also transform the mobility of nodes into node behaviors to reduce the impact of mobility on the network. Our extensive simulation results show that our proposed strategies can apply to large-size and high-mobility networks, while achieving satisfactory results for data access.

Data-centric networks provide content instead of address (what vs. where) based communication primitives, and have been argued to be the proper candidate for data dissemination in high mobility vehicular networks (e.g., delivering road side accident video clips to affected drivers in both directions). However, current Medium Access Control (MAC) layers filter incoming frames based on destination addresses, not content. The data-centric network community has resorted to MAC broadcast, with high and greatly varying frame loss rates. We propose V-MAC, a data-centric MAC layer that filters frames by content. It supports one to many multicast at MAC level, and ensures a uniform and controllable small frame loss rate across all receivers, despite their varying reception qualities. We have created a V-MAC prototype using Raspberry Pis and WiFi dongles. Experiments under extremely noisy environment show that it reduces frame loss from 50% (broadcast) to less than 10%, and consistently among multiple receivers.

While existing visual recognition approaches, which rely on 2D images to train their underlying models, work well for object classification, recognizing the changing state of a 3D object requires addressing several additional challenges. This paper proposes an active visual recognition approach to this problem, leveraging camera pose data available on mobile devices. With this approach, the state of a 3D object, which captures its appearance changes, can be recognized in real time. Our novel approach selects informative video frames filtered by 6-DOF camera poses to train a deep learning model to recognize object state. We validate our approach through a prototype for Augmented Reality-assisted hardware maintenance.

User authentication on smartphones must satisfy both security and convenience, an inherently difficult balancing art. Apple's FaceID is arguably the latest of such efforts, at the cost of additional hardware (e.g., dot projector, flood illuminator and infrared camera). We propose a novel user authentication system EchoPrint, which leverages acoustics and vision for secure and convenient user authentication, without requiring any special hardware. EchoPrint actively emits almost inaudible acoustic signals from the earpiece speaker to "illuminate" the user's face and authenticates the user by the unique features extracted from the echoes bouncing off the 3D facial contour. To combat changes in phone-holding poses thus echoes, a Convolutional Neural Network (CNN) is trained to extract reliable acoustic features, which are further combined with visual facial landmark locations to feed a binary Support Vector Machine (SVM) classifier for final authentication. Because the echo features depend on 3D facial geometries, EchoPrint is not easily spoofed by images or videos like 2D visual face recognition systems. It needs only commodity hardware, thus avoiding the extra costs of special sensors in solutions like FaceID. Experiments with 62 volunteers and non-human objects such as images, photos, and sculptures show that EchoPrint achieves 93.75% balanced accuracy and 93.50% F-score, while the average precision is 98.05%, and no image/video based attack is observed to succeed in spoofing.

The application of wireless charging technology in traditional battery-powered wireless sensor networks (WSNs) grows rapidly recently. Although previous studies indicate that the technology can deliver energy reliably, it still faces regulatory mandate to provide high power density without incurring health risks. In particular, in clustered WSNs there exists a mismatch between the high energy demands from cluster heads and the relatively low energy supplies from wireless chargers. Fortunately, solar energy harvesting can provide high power density without health risks. However, its reliability is subject to weather dynamics. In this paper, we propose a hybrid framework that combines the two technologies - cluster heads are equipped with solar panels to scavenge solar energy and the rest of nodes are powered by wireless charging. We divide the network into three hierarchical levels. On the first level, we study a discrete placement problem of how to deploy solar-powered cluster heads that can minimize overall cost and propose a distributed 1:61(1+ϵ)2-approximation algorithm for the placement. Then, we extend the discrete problem into continuous space and develop an iterative algorithm based on the Weiszfeld algorithm. On the second level, we establish an energy balance in the network and explore how to maintain such balance for wireless-powered nodes when sunlight is unavailable. We also propose a distributed cluster head re-selection algorithm. On the third level, we first consider the tour planning problem by combining wireless charging with mobile data gathering in a joint tour. We then propose a polynomial-time scheduling algorithm to find appropriate hitting points on sensors' transmission boundaries for data gathering. For wireless charging, we give the mobile chargers more flexibility by allowing partial recharge when energy demands are high. The problem turns out to be a Linear Program. By exploiting its particular structure, we propose an efficient algorithm that can achieve near-optimal solutions. Our extensive simulation results demonstrate that the hybrid framework can reduce battery depletion by 20 percent and save vehicles' moving cost by 25 percent compared to previous works. By allowing partial recharge, battery depletion can be further reduced at a slightly increased cost. The results also suggest that we can reduce the number of high-cost mobile chargers by deploying more low-cost solar-powered sensors.

The lack of digital floor plans in most buildings has become a huge obstacle to pervasive indoor location based services (LBS). Recently there has been quite some research that leverages various sensing data such as inertial, WiFi and images from ubiquitous mobile devices (e.g., smartphones) to construct floor plans at large scale and low costs. Although great efforts are made to improve the accuracy and robustness against sensing data errors and noises, the quality of reconstructed maps is still limited. In this paper, we explore the hidden geometric structure information of indoor environments, such as collinearity of doors along hallways, right-angle corners, and polygon/circular shapes of rooms to optimize floor plans. Such prior knowledge about building structures provide new spatial relationships among floor plan elements. Thus we can further improve the quality of reconstructed maps. Real experiments in two large buildings show that 90-percentile landmark location errors are reduced by more than 50% to within 1m, and most orientation errors are corrected. The overall shape of the map has become much closer to the ground truth as well.

Edge devices (e.g., smartphones, tablets, connected vehicles, IoT nodes) with sensing, storage and communication resources are increasingly penetrating our environments. Many novel applications can be created when nearby peer edge devices share data. Caching can greatly improve the data availability, retrieval robustness and latency. In this paper, we study the unique issue of caching fairness in edge environment. Due to distinct ownership of peer devices, caching load balance is critical. We consider fairness metrics and formulate an integer linear programming problem, which is shown as summation of multiple Connected Facility Location (ConFL) problems. We propose an approximation algorithm leveraging an existing ConFL approximation algorithm, and prove that it preserves a 6.55 approximation ratio. We further develop a distributed algorithm where devices exchange data reachability and identify popular candidates as caching nodes. Extensive evaluation shows that compared with existing wireless network caching algorithms, our algorithms significantly improve data caching fairness, while keeping the contention induced latency similar to the best existing algorithms.

The proliferation and daily congregation of modern mobile devices have created abundant opportunities for peer edge devices to share valuable data with each other. The short contact durations, relatively small sharing sizes, and uncertain data availability, demand agile, light weight peer based data sharing. In this paper, we propose Peer Data Sharing (PDS) that enables edge devices to discover which data exist in nearby peers, and retrieve interested data robustly and efficiently. PDS uses novel lingering queries, mixedcast and en-route message rewriting techniques to minimize redundant transmissions and maximize opportunistic overhearing thus caching in data discovery and retrieval. Extensive evaluations based on an Android prototype show that PDS discovers and retrieves almost 100% data in tens of seconds, and remains robust despite wireless contention, simultaneous consumer requests and user mobility.

Lacking of floor plans is a fundamental obstacle to ubiquitous indoor location-based services. Recent work have made significant progress to accuracy, but they largely rely on slow crowdsensing that may take weeks or even months to collect enough data. In this paper, we propose Knitter that can generate accurate floor maps by a single random user's one hour data collection efforts. Knitter extracts high quality floor layout information from single images, calibrates user trajectories and filters outliers. It uses a multi-hypothesis map fusion framework that updates landmark positions/orientations and accessible areas incrementally according to evidences from each measurement. Our experiments on 3 different large buildings and 30+ users show that Knitter produces correct map topology, and 90-percentile landmark location and orientation errors of 3 ~ 5m and 4 ~ 6°, comparable to the state-of-the-art at more than 20× speed up: data collection can finish in about one hour even by a novice user trained just a few minutes.

The rapid penetration of mobile devices has provided ample opportunities for mobile devices to exchange sensing data on a peer basis without any centralized backend. In this paper, we design a peer based data exchanging model, where relay nodes move to certain locations to connect data providers and consumers to facilitate data delivery. Both relays and data providers can gain rewards from consumers who are willing to pay for the data. We first prove the problem of relay node assignment is NP-hard, and provide a centralized optimal method to decide which relay nodes goes to which location with an approximation ratio. Then we define an autonomous compensation game to allow relays make individual decisions without any central authority. We derive a sufficient and necessary condition for the existence of Nash equilibrium. We analyze and compare this distributed game to the centralized social optimal solution, and show that the game incurs small bounded social costs, and efficient under various network sizes, numbers of providers, consumers, and device mobility.

This demo presents BatMapper, an acoustics sensing technology for fast, fine-grained and low cost floor plan construction. BatMapper operates by emitting sound signal and capturing its reflections by two microphones on smartphones. We develop robust probabilistic echo-object association and outlier removal algorithms to identify the correspondence between distances and objects, thus the geometry of corridors. We compensate minute hand sway movements to identify small surface recessions, thus detecting doors automatically. Additionally, we leverage structure cues in indoor environments for user trace calibration. The demo will enable any person to hold the smartphone and walk along a corridor to map the corridor shape and detect doors in real-time.

The lack of digital floor plans is a huge obstacle to pervasive indoor location based services (LBS). Recent floor plan construction work crowdsources mobile sensing data from smartphone users for scalability. However, they incur long time (e.g., weeks or months) and tremendous efforts in data collection, and many rely on images thus suffering technical and privacy limitations. In this paper, we propose BatMapper, which explores a previously untapped sensing modality -- acoustics -- for fast, fine grained and low cost floor plan construction. We design sound signals suitable for heterogeneous microphones on commodity smartphones, and acoustic signal processing techniques to produce accurate distance measurements to nearby objects. We further develop robust probabilistic echo-object association, recursive outlier removal and probabilistic resampling algorithms to identify the correspondence between distances and objects, thus the geometry of corridors and rooms. We compensate minute hand sway movements to identify small surface recessions, thus detecting doors automatically. Experiments in real buildings show BatMapper achieves 1-2cm distance accuracy in ranges up around 4m; a 2-3 minute walk generates fine grained corridor shapes, detects doors at 92% precision and 1~2m location error at 90-percentile; and tens of seconds of measurement gestures produce room geometry with errors <0.3m at 80-percentile, at 1-2 orders of magnitude less data amounts and user efforts.

Continuous tracking of the device location in 3D space is a popular form of user input, especially for virtual/augmented reality (VR/AR), video games and health rehabilitation. Conventional inertial based approaches are well known for inaccuracy caused by large error drifts. Computer vision approaches can produce accuracy tracking but have privacy concerns and are subject to lighting conditions and computation complexity. Recent work exploits accurate acoustic distance measurements for high precision tracking. However, they require additional hardware (e.g., multiple external speakers), which adds to the costs and installation efforts, thus limiting the convenience and usability. In this paper, we propose BatTracker, which incorporates inertial and acoustic data for robust, high precision and infrastructure-free tracking in indoor environments. BatTracker leverages echoes from nearby objects and uses distance measurements from them to correct error accumulation in inertial based device position prediction. It incorporates Doppler shifts and echo amplitudes to reliably identify the association between echoes and objects despite noisy signals from multi-path reflection and cluttered environment. A probabilistic algorithm creates, prunes and evolves multiple hypotheses based on measurement evidences to accommodate uncertainty in device position. Experiments in real environments show that BatTracker can track a mobile device's movements in 3D space at sub-cm level accuracy, comparable to the state-of-the-art infrastructure based approaches, while eliminating the needs of any additional hardware.

With the development of distributed energy generators and storages, the sustainability of a microgrid comprised of multiple electricity users is significantly increased. Maximizing the efficiency of generated renewable energy is vital to running a sustainable microgrid as it indicates reducing the usage of thermal electricity purchased from the macrogrid. To this end, the excessive renewable energy of a user should be shared with others who are short of energy. Unfortunately, coordinating the transfers of renewable energy among the users in a microgrid is particularly difficult, given the rational nature of users, the stochastic nature of renewable energy and the dynamic nature of energy demand of each user. In this paper, we consider the coupled problem of maximizing the renewable energy efficiency of a sustainable microgrid as well as stimulating rational users to share excessive renewable energy. We propose a near-optimal scheduling algorithm, which determines the amounts of renewable energy transferred among users in an online fashion. We also design an efficient pricing mechanism for the trade of energy among users based on double auction. We rigorously prove that our online scheduling algorithm is approximately optimal and the pricing mechanism guarantees the property of individual rationality of users. Comprehensive simulation results demonstrate the efficacy of our online algorithm and incentive mechanism.

Although location awareness and turn-by-turn instructions are prevalent outdoors due to GPS, we are back into the darkness in uninstrumented indoor environments such as underground parking structures. We get confused, disoriented when driving in these mazes, and frequently forget where we parked, ending up circling back and forth upon return. In this paper, we propose VeTrack, asmartphone-only system that tracks the vehicle’s location in real time using the phone’s inertial sensors. It does not require any environment instrumentation or cloud backend. It uses a novel “shadow” trajectory tracing method to accurately estimate phone’s and vehicle’s orientations despite their arbitrary poses and frequent disturbances. We develop algorithms in a Sequential Monte Carlo framework to represent vehicle states probabilistically, and harness constraints by the garage map and detected landmarks to robustly infer the vehicle location. We also find landmark (e.g., speed bumps, turns) recognition methods reliable against noises, disturbances from bumpy rides, and even hand-held movements. We implement a highly efficient prototype and conduct extensive experiments in multiple parking structures of different sizes and structures, and collect data with multiple vehicles and drivers. We find that VeTrack can estimate the vehicle’s real time location with almost negligible latency, with error of$2\sim 4$parking spaces at the 80th percentile.

Wireless charging has provided a convenient alternative to renew nodes' energy in wireless sensor networks. Due to physical limitations, previous works have only considered recharging a single node at a time, which has limited efficiency and scalability. Recent advances on multi-hop wireless charging is gaining momentum and provides fundamental support to address this problem. However, existing single-node charging designs do not consider and cannot take advantage of such opportunities. In this paper, we propose a new framework to enable multi-hop wireless charging using resonant repeaters. First, we present a realistic model that accounts for detailed physical factors to calculate charging efficiencies. Second, to achieve balance between energy efficiency and data latency, we propose a hybrid data gathering strategy that combines static and mobile data gathering to overcome their respective drawbacks and provide theoretical analysis. Then, we formulate multi-hop recharge schedule into a bi-objective NP-hard optimization problem. We propose a two-step approximation algorithm that first finds the minimum charging cost and then calculates the charging vehicles' moving costs with bounded approximation ratios. Finally, upon discovering more room to reduce the total system cost, we develop a post-optimization algorithm that iteratively adds more stopping locations for charging vehicles to further improve the results while ensuring none of the nodes will deplete battery energy. Our extensive simulations show that the proposed algorithms can handle dynamic energy demands effectively, and can cover at least three times of nodes and reduce service interruption time by an order of magnitude compared to the single-node charging scheme.

In many mobile sensing applications, devices need to discover new neighbors and maintain the rendezvous with known neighbors continuously. Due to the limited energy supply, these devices have to duty cycle their radios to conserve the energy and bandwidth, making neighbor discovery and rendezvous maintenance even more challenging. To date, the main mechanism for device discover and rendezvous maintenance in existing solutions is pairwise, direct one-hop communication. We argue that such pairwise direct communication is sufficient but not necessary: there exist unnecessary active slots that can be eliminated, without affecting discovery and rendezvous. In this work, we propose a novel concept ofextended quorum system, which leveragesindirectdiscovery to further conserve energy. Specifically, we usequorum graphto capture all possible information flow paths where knowledge about known-neighbors can propagate among devices. By eliminating redundant paths, we can reduce the number of active slots significantly. Since a quorum graph can characterize arbitrary active schedules of mobile devices, our work can be broadly used to improve many existing quorum-based discovery and rendezvous solutions. We comprehensively evaluate$EQS$in three different scales of networks, and the results show that$EQS$reduces as much as 55 percent energy consumption with a maximal 5 percent increase in latency for existing solutions. To test the real-world values of$EQS$, we further propose a taxicab dispatching application called$EQS$-dispatch to navigate taxicab drivers to the area with less competition based on the discovery results of nearby taxicabs.

Many web applications provide secondary authentication methods, i.e., secret questions (or password recovery questions), to reset the account password when a user's login fails. However, the answers to many such secret questions can be easily guessed by an acquaintance or exposed to a stranger that has access to public online tools (e.g., online social networks); moreover, a user may forget her/his answers long after creating the secret questions. Today's prevalence of smartphones has granted us new opportunities to observe and understand how the personal data collected by smartphone sensors and apps can help create personalized secret questions without violating the users’ privacy concerns. In this paper, we present aSecret-Question based Authenticationsystem, called “Secret-QA”, that creates a set of secret questions on basic of people's smartphone usage. We develop a prototype on Android smartphones, and evaluate the security of the secret questions by asking the acquaintance/stranger who participates in our user study to guess the answers with and without the help of online tools; meanwhile, we observe the questions’ reliability by asking participants to answer their own questions. Our experimental results reveal that the secret questions related to motion sensors, calendar, app installment, and part of legacy app usage history (e.g., phone calls) have the best memorability for users as well as the highest robustness to attacks.

Since GPS signal is not applicable indoors, vehicle tracking has proven a hassle in underground parking structures. Recent solutions highly rely on floor map to constraint inertial sensors noises. In this paper, we propose VeMap, a road map construction system using only smartphones inside vehicles. It saves effort-intensive and time-consuming business negotiations with building operators, and expensive personnel cost to gather such data. It fuses multiple sensors to calibrate inertial noises, and uses Dynamic Time Warping to align multiple trajectories. We represent the floor plan with occupancy grid mapping, and explore a vision-mobile joint algorithm to extract its skeleton and form the road map. VeMap is tested in a 250mx90m parking structure, and it can be directly used for driving navigation to free parking spaces.

As air pollution is becoming a serious problem in developing nations, governments try to track and solve this problem by monitoring air pollution. With the proliferation of smartphones, mobile crowdsensing becomes a promising paradigm for monitoring fine-grained air pollution in urban areas. As existing studies have shown that pollutant concentrations have inherent spatiotemporal correlations, compressive sensing is an effective technology to reduce the amount of data collected through crowdsensing. In a practical crowdsensing application, incentives are expected by smartphone users for contributing sensing data. However, how to design incentives to collect high- quality sensing data with low costs is difficult in compressive crowdsensing. In this work, we propose an iterative scheme for the process of crowdsensing-based air pollution monitoring, where incentives are updated online according to the distribution of collected sensing data. Comprehensive simulations have been conducted to demonstrate the efficacy of our proposed scheme.

Despite significant research efforts and great advances on Mobile CrowdSensing (MCS), building MCS applications remains difficult. In this paper, we develop and run Dining Halls on Live (DHOL), a campus dining population density monitoring system over several months. We make a holistic reality examination, discover key technical and practical difficulties, develop effective solutions and share our experiences and insights. We find two main obstacles on data fusion and incentive design: insufficient data quantity/quality and ``irrational'' user behavior. We develop effective methods by combining historical and real time data, and allocating a given budget among users to address them. We also conduct a detailed user survey to identify reasons behind interesting discoveries, important practical difficulties in acquiring sufficient users and location data, and share our experiences dealing with them. Our main insight is that insufficient data quantity/quality and ``irrational'' user behavior demand practical yet effective data fusion and incentive mechanisms, and one must provide values to users to acquire and retain a large user base.

Digital garage maps are the basis for future vehicle navigation services such as smart parking management that displays the availability of parking spaces. It can direct drivers to empty ones, avoiding any searching, circulating in large, complex parking structures. However, such maps are not currently available, making it impossible to deploy smart parking management. Conducting manual survey incurs tremendous amount of human efforts, and cannot scale to large numbers of garages. In this paper, we propose three algorithms, Sequential Merging, Points Clustering and Segments Matching that can automatically construct complete and accurate garage maps using data crowdsensed from drivers. Upon entering and leaving the garage, the driver's smartphone collects inertial data, which are used to generate the vehicle's trajectory. Our algorithms fuse together these trajectories to recreate the size, layout of the garage. We compare the performance of the three algorithms using different garages. We find that Points Clustering is robust to trajectory errors, with F-score above 0.95 for trajectory length error up to 2 meters, Segments Matching can handle partial trajectories with arbitrary start/end locations, and it constructs the same map using trajectories much shorter than those needed by the other two algorithms.

With the development of renewable energy generators and electricity storages, microgrids become a promising technology of the smart grid. Maximizing the usage of renewable energy is vital to running a microgrid as it indicates reduction of the usage of thermal electricity purchased from the macrogrid. To this end, the excessive renewable energy of a user should be transferred to other users who need energy. Unfortunately, coordinating the transfers of renewable energy among the users in the microgrid is particularly difficult due to the stochastic nature of renewable energy, and the dynamic energy demand of each user. In this paper, we consider the problem of maximizing the long-term renewable energy usage by exchanging excessive renewable energy among users in a microgrid. We propose an online control algorithm which determines the amounts of renewable energy transferred among users in an online fashion. We rigorously prove that our online control algorithm is approximately optimal. We have conducted comprehensive simulation results that demonstrate the efficacy of our online algorithm.

Recently, there have been a growing number of applications that power wireless sensor networks (WSNs) by wireless charging technology. Although previous studies indicate that wireless charging can deliver energy reliably, it still faces regulatory challenges to provide high power density without incurring health risks. In particular, in clustered WSNs there exists a mismatch between the high energy demands from cluster heads and the relatively low energy supplies that wireless charging can provide. Fortunately, solar energy harvesting can provide high power density which is also risk-free. However, it is subject to weather dynamics. Therefore, in this paper, we propose a hybrid framework that combines the two technologies - cluster heads are equipped with solar panels to scavenge solar energy and the rest of nodes are powered by wireless charging. First, we study a placement problem on how to deploy solar-powered cluster heads that can minimize overall cost and propose a distributed 1.61(1 + ϵ)2-approximation algorithm for the placement. Second, we establish an energy balance in the network and explore how to maintain such balance when sunlight is unavailable. Third, we consider combining wireless charging and mobile data gathering in a joint tour in such networks, and propose a polynomial-time scheduling algorithm. Our extensive simulation demonstrates that the hybrid framework can reduce battery depletion by 20% and save system cost by 25% compared to previous results.

Several recent works have studied mobile vehicle scheduling to recharge sensor nodes via wireless energy transfer technologies. Unfortunately, most of them overlooked important factors of the vehicles' moving energy consumption and limited recharging capacity, which may lead to problematic schedules or even stranded vehicles. In this paper, we consider the recharge scheduling problem under such important constraints. To balance energy consumption and latency, we employ one dedicated data gathering vehicle and multiple charging vehicles. We first organize sensors into clusters for easy data collection, and obtain theoretical bounds on latency. Then we establish a mathematical model for the relationship between energy consumption and replenishment, and obtain the minimum number of charging vehicles needed. We formulate the scheduling into a Profitable Traveling Salesmen Problem that maximizes profit - the amount of replenished energy less the cost of vehicle movements, and prove it is NP-hard. We devise and compare two algorithms: a greedy one that maximizes the profit at each step; an adaptive one that partitions the network and forms Capacitated Minimum Spanning Trees per partition. Through extensive evaluations, we find that the adaptive algorithm can keep the number of nonfunctional nodes at zero. It also reduces transient energy depletion by 30-50 percent and saves 10-20 percent energy. Comparisons with other common data gathering methods show that we can save 30 percent energy and reduce latency by two orders of magnitude.

Mainstream indoor localization technologies rely on RF signatures that require extensive human efforts to measure and periodically recalibrate signatures. The progress to ubiquitous localization remains slow. In this study, we explore Sextant, an alternative approach that leverages environmental reference objects such as store logos. A user uses a smartphone to obtain relative position measurements to such static reference objects for the system to triangulate the user location. Sextant leverages image matching algorithms to automatically identify the chosen reference objects by photo-taking, and we propose two methods to systematically address image matching mistakes that cause large localization errors. We formulate the benchmark image selection problem, prove its NP-completeness, and propose a heuristic algorithm to solve it. We also propose a couple of geographical constraints to further infer unknown reference objects. To enable fast deployment, we propose a lightweight site survey method for service providers to quickly estimate the coordinates of reference objects. Extensive experiments have shown that Sextant prototype achieves 2-5 m accuracy at 80-percentile, comparable to the industry state-of-the-art, while covering a 150 x 75 m mall and 300 x 200m train station requires a one time investment of only 2-3 man-hours from service providers.

The lack of floor plans is a critical reason behind the current sporadic availability of indoor localization service. Service providers have to go through effort-intensive and time-consuming business negotiations with building operators, or hire dedicated personnel to gather such data. In this paper, we propose Jigsaw, a floor plan reconstruction system that leverages crowdsensed data from mobile users. It extracts the position, size, and orientation information of individual landmark objects from images taken by users. It also obtains the spatial relation between adjacent landmark objects from inertial sensor data, then computes the coordinates and orientations of these objects on an initial floor plan. By combining user mobility traces and locations where images are taken, it produces complete floor plans with hallway connectivity, room sizes, and shapes. It also identifies different types of connection areas (e.g., escalators and stairs) between stories, and employs a refinement algorithm to correct detection errors. Our experiments on three stories of two large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about 1~2m and 5~9°, while the hallway connectivity and connection areas between stories are 100 percent correct.

In this paper, we present TapLock as a smartphone password system that exploits the finger tap events on capacitive touch screens for increasing the password's resilience to shoulder-surfing attacks (where the password input by a user can be easily observed by a bystander over the user's shoulder). TapLock captures the size and the axis length of the finger touch area on the phone screen for creating a password, which cannot be easily observed by a shoulder surfer. Our user study shows that TapLock has several advantages over existing smartphone password systems, including its strong attack resilience, small authentication delay, and haptic input feedback that improves the usability.

This paper presents a method to design optimized local control strategies for Cyber-Physical Systems that produce reliable data models for social applications. Data models have different semantics and abstraction levels. The local control strategies manage ad-hoc nano-clouds of embedded computing and communication nodes (CCNs) used for data collection, modeling, and communication. Control strategies consider tradeoffs defined by the resource constraints of embedded CCNs (e.g., computing power, communication bandwidth, and energy), assurance requirements (e.g., robustness) of the models, and privacy of users. Experiments evaluate and demonstrate the effectiveness of the control strategies for nano-clouds composed of smart voice-based interaction-tracking badges.

Many smartphone apps routinely gather various private user data and send them to advertisers. Despite recent study on protection mechanisms and analysis on apps' behavior, the understanding about the consequences of such privacy losses remains limited. In this paper we investigate how much an advertiser can infer about users' social and community relationships by combining data from multiple applications and across many users. After one month's user study involving about 200 most popular Android apps, we find that an advertiser can infer 90% of the social relationships. We further propose a privacy leakage inference framework and use real mobility traces and Foursquare data to quantify the consequences of privacy leakage. We find that achieving 90% inference accuracy of the social and community relationships requires merely 3 weeks' user data. The discoveries underscore the importance of early adoption of privacy protection mechanisms.

Wireless charging has provided a convenient alternative to renew sensors' energy in wireless sensor networks. Due to physical limitations, previous works have only considered recharging a single node at a time, which has limited efficiency and scalability. Recent advance on multi-hop wireless charging is gaining momentum to provide fundamental support to address this problem. However, existing single-node charging designs do not consider and cannot take advantage of such opportunities. In this paper, we propose a new framework to enable multi-hop wireless charging using resonant repeaters. First, we present a realistic model that accounts for detailed physical factors to calculate charging efficiencies. Second, to achieve balance between energy efficiency and data latency, we propose a hybrid data gathering strategy that combines static and mobile data gathering to overcome their respective drawbacks and provide theoretical analysis. Then we formulate multi-hop recharge schedule into a bi-objective NP-hard optimization problem. We propose a two-step approximation algorithm that first finds the minimum charging cost and then calculates the charging vehicles' moving costs with bounded approximation ratios. Finally, upon discovering more room to reduce the total system cost, we develop a post-optimization algorithm that iteratively adds more stopping locations for charging vehicles to further improve the results. Our extensive simulations show that the proposed algorithms can handle dynamic energy demands effectively, and can cover at least three times of nodes and reduce service interruption time by an order of magnitude compared to the single-node charging scheme.

Although location awareness and turn-by-turn instructions are prevalent outdoors due to GPS, we are back into the darkness in uninstrumented indoor environments such as underground parking structures. We get confused, disoriented when driving in these mazes, and frequently forget where we parked, ending up circling back and forth upon return.In this paper, we propose VeTrack, a smartphone-only system that tracks the vehicle's location in real time using the phone's inertial sensors. It does not require any environment instrumentation or cloud backend. It uses a novel "shadow" tracing method to accurately estimate the vehicle's trajectories despite arbitrary phone/vehicle poses and frequent disturbances. We develop algorithms in a Sequential Monte Carlo framework to represent vehicle states probabilistically, and harness constraints by the garage map and detected landmarks to robustly infer the vehicle location. We also find landmark (e.g., speed bumps, turns) recognition methods reliable against noises, disturbances from bumpy rides and even hand-held movements. We implement a highly efficient prototype and conduct extensive experiments in multiple parking structures of different sizes and structures, with multiple vehicles and drivers. We find that VeTrack can estimate the vehicle's real time location with almost negligible latency, with error of 2-4 parking spaces at 80-percentile.

As a supporting primitive of many mobile applications, neighbor discovery identifies nearby devices so that they can exchange information and collaborate in a peer-to-peer manner. To date, discovery schemes trade a long latency for energy efficiency and require a collaborative duty cycle pattern, and thus they are not suitable for interactive mobile applications where a user is unable to configure others’ devices. In this article, we propose Acc , which serves as an on-demand generic discovery accelerating middleware for many deterministic neighbor discovery schemes. Acc leverages the discovery capabilities of neighbor devices, supporting both direct and indirect neighbor discoveries. Further, we present a proactive online rendezvous maintenance mechanism, which is used to reduce delays for the detection of leaving of neighbors. Our evaluations show that Acc -assisted discovery schemes reduce latency by up to 51.8% compared to schemes consuming the same amount of energy. More importantly, to prove the real-world value of Acc , we further present and evaluate a Crowd-Alert application where Acc is employed by taxi drivers to accelerate selection of a direction with fewer competing taxis and more potential passengers, based on a 280GB dataset of more than 14,000 taxis in Shenzhen, the most crowded city in China.

The rapid growth in the resources and processing power of FPGA has made it more and more attractive as accelerator platforms. Due to its high performance, the PCIe bus is the preferred interconnection between the host computer and loosely-coupled FPGA accelerators. To fully utilize the high performance of PCIe, developers have to write significant amount of PCIe related code. In this paper, we present the design of EPEE, an efficient PCIe communication library that can integrate with hosts easily to alleviate developers from such burden. It is not trivial to make a PCIe communication library highly efficient and easy-host-integration simultaneously. We have identified several challenges in the work: 1) the conflict between efficiency and functionality; 2) the support for multi-clock domain interface; 3) the solution to DMA data out-of-order transfer; 4) the portability. Few existing systems have addressed all the challenges. EEPE has a highly efficient core library that is extensible. We provide a set of APIs abstracted at high levels to ease the learning curve of developers, and divide the hardware library into device dependent and independent layers for portability. We have implemented EEPE in various generations of Xilinx FPGAs with up to 12.7 Gbps half-duplex and 20.8 Gbps full-duplex data rates in PCIe Gen2X4 mode (79.4% and 64.0% of the theoretical maximum data rates respectively). EEPE has already been used in four different FPGA applications, and it can be integrated with high-level synthesis tools, in particular Vivado-HLS.

A high-performance interconnection between a host processor and FPGA accelerators is in much demand. Among various interconnection methods, a PCIe bus is an attractive choice for loosely coupled accelerators. Because there is no standard host-FPGA communication library, FPGA developers have to write significant amounts of PCIe related code at both the FPGA side and the host processor side. A high-performance host-FPGA PCIe communication library holds the key to broadening the use of FPGA accelerators. In this paper we target efficiency and flexibility as two important features in such a library. We discuss the challenges in providing these features, and present our solution to these challenges. We propose EPEE, an efficient and flexible host-FPGA PCIe communication library and describe its design. We implemented EPEE in various generations of Xilinx FPGAs with up to 26.24 Gbps half-duplex and 43.02 Gbps full-duplex aggregate throughput in the PCIe Gen2 X8 mode; these are at the best utilization levels that a host-FPGA PCIe library can achieve. The EPEE library has been integrated into four different FPGA applications with different data usage patterns in various institutes.

Existing mainstream indoor localization technologies mainly rely on RF signatures and thus incur significant and recurring labor cost to measure the time-varying signature map. We have proposed a smartphone localization system using the embedded gyroscope for triangulation from nearby physical features (e.g., store logos) recognized from photo-taking. It requires a much reduced and one-time measurement, while incurs uncertain localization errors. In this paper, we propose two methods to systematically address image matching errors that cause unrecognized physical features and large errors in our system. We formulate the optimal benchmark image selection problem and propose a heuristic algorithm that finds the best benchmark images for high matching accuracy. We propose a couple of geographical constraints to further infer unknown physical features based on the observation that the features chosen by the user are close together. Experiments in a 150 × 75m shopping mall, 300 × 200m train station show that dramatically we cut down both maximum and general localization errors, and achieve 2-8m accuracy at 80-percentile even with only one benchmark image on the phone.

Mainstream indoor localization technologies rely on RF signatures that require extensive human efforts to measure and periodically re-calibrate. Although recent crowdsourcing based work has started to address the issue, incentives are still lacking for wide user adoption. Thus the progress to ubiquitous localization remains slow. In this paper, we explore an alternative approach that leverages environmental physical features such as store logos or wall posters. A user uses a smartphone to obtain relative position measurements to such static reference points for the system to triangulate the user location. We study the principle of such localization, determine the suitable sensor, and devise guidelines for the user to choose reference points for better accuracy. To enable fast deployment, we propose a lightweight site survey method for service providers to quickly estimate the coordinates of reference points. We incorporate and enhance image matching algorithms with a heuristic technique to automatically identify chosen reference points at high accuracy. Extensive experiments have shown that the prototype achieves 4-5m accuracy at 80-percentile, comparable to the industry state-of-the-art, while covering a 150×75m mall and 300×200m train station requires a one time investment of only 2-3 man-hours from service providers.

The lack of floor plans is a critical reason behind the current sporadic availability of indoor localization service. Service providers have to go through effort-intensive and time-consuming business negotiations with building operators, or hire dedicated personnel to gather such data. In this paper, we propose Jigsaw, a floor plan reconstruction system that leverages crowdsensed data from mobile users. It extracts the position, size and orientation information of individual landmark objects from images taken by users. It also obtains the spatial relation between adjacent landmark objects from inertial sensor data, then computes the coordinates and orientations of these objects on an initial floor plan. By combining user mobility traces and locations where images are taken, it produces complete floor plans with hallway connectivity, room sizes and shapes. Our experiments on 3 stories of 2 large shopping malls show that the 90-percentile errors of positions and orientations of landmark objects are about 1~2m and 5~9°, while the hallway connectivity is 100% correct.

Federated coalition networks are formed by interconnected nodes belonging to different friendly-but-curious parties cooperating for common objectives. Each party has its policy regarding what information may be accessed by which other parties. Data delivery in coalition networks must provide both confidentiality and robustness. First, data should remain confidential when passing through intermediate nodes belonging to parties not authorized to see its content. Second, data delivery has to be robust against dynamic topology changes caused by frequent node churn and failures. We utilize the technique of linear network coding to transform the original data into multiple coded packets and send them along different paths in a way such that no other party can reconstruct the data. This lightweight approach provides confidentiality and robustness for friendly-but-curious coalitions with much less complexity than cryptography methods. In addition, we formulate an optimization problem to find minimum-cost paths, and use column generation framework to address the huge number of variables. Based on the proposed algorithms, we develop a Robust Confidentiality Preserving (R-CP) data delivery protocol. Our evaluation demonstrates that the proposed method can find the optimum solution in several seconds for networks of a few thousands nodes, and deliver data at a high success rate.

Several recent works have studied the schedule for mobile vehicles to recharge sensor nodes via wireless energy transfer technologies. Unfortunately, most of them overlooked the important factors of the vehicles' moving energy consumption and limited recharging capacity. These oversights may lead to problematic schedules or even stranded vehicles. In this paper, we study the recharging schedule that maximizes the recharging profit - the amount of replenished energy less the cost of vehicle movements - under these important constraints. We first derive the minimum number of vehicles needed for energy neutral condition and discover a set of desired network properties. Then we formulate the recharge schedule optimization into a Profitable Traveling Salesmen Problem with capacity and battery deadline constraints, which we prove to be NP-hard. We propose two algorithms to solve the problem. The first one is a greedy algorithm that maximizes the recharge profit at each step; the second one first adaptively partitions the network based on recharge requests, then forms Capacitated Minimum Spanning Tree in each partition followed by route improvements. Finally, we evaluate and compare the performance of proposed algorithms and validate the correctness of theoretical results through extensive simulations. Given a sufficient number of vehicles, the adaptive algorithm can keep the number of nonfunctional nodes at zero. Compared to the greedy algorithm, it reduces the percentage of transient energy depletion by 30-50% with 10-20% energy saving on vehicles.

We present VeLoc, a smartphone-based vehicle localization approach that tracks the vehicle's parking location without GPS or WiFi signals. It uses only the embedded accelerometer and gyroscope sensors. VeLoc harnesses constraints imposed by the map and landmarks (e.g., speed bumps) recognized from inertial data, employs a Bayesian filtering framework to estimate the location of the vehicle. We have conducted experiments in three parking structures of different sizes and configurations, using three vehicles and three kinds of driving styles. We find that VeLoc can always localize the vehicle within 10m, which is sufficient for the driver to trigger a honk using the car key.

Highly accurate indoor localization of smartphones is critical to enable novel location based features for users and businesses. In this paper, we first conduct an empirical investigation of the suitability of WiFi localization for this purpose. We find that although reasonable accuracy can be achieved, significant errors (e.g., 6 8m) always exist. The root cause is the existence of distinct locations with similar signatures, which is a fundamental limit of pure WiFi-based methods. Inspired by high densities of smartphones in public spaces, we propose a peer assisted localization approach to eliminate such large errors. It obtains accurate acoustic ranging estimates among peer phones, then maps their locations jointly against WiFi signature map subjecting to ranging constraints. We devise techniques for fast acoustic ranging among multiple phones and build a prototype. Experiments show that it can reduce the maximum and 80-percentile errors to as small as 2m and 1m, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.

Using vehicles equipped with wireless energy transmission technology to recharge sensor nodes over the air is a game-changer for traditional wireless sensor networks. The recharging policy regarding when to recharge which sensor nodes critically impacts the network performance. So far only a few works have studied such recharging policy for the case of using a single vehicle. In this paper, we propose NETWRAP, an NDN based Real Time Wireless Recharging Protocol for dynamic wireless recharging in sensor networks. The real-time recharging framework supports single or multiple mobile vehicles. Employing multiple mobile vehicles provides more scalability and robustness. To efficiently deliver sensor energy status information to vehicles in real-time, we leverage concepts and mechanisms from named data networking (NDN) and design energy monitoring and reporting protocols. We derive theoretical results on the energy neutral condition and the minimum number of mobile vehicles required for perpetual network operations. Then we study how to minimize the total traveling cost of vehicles while guaranteeing all the sensor nodes can be recharged before their batteries deplete. We formulate the recharge optimization problem into a Multiple Traveling Salesman Problem with Deadlines (m-TSP with Deadlines), which is NP-hard. To accommodate the dynamic nature of node energy conditions with low overhead, we present an algorithm that selects the node with the minimum weighted sum of traveling time and residual lifetime. Our scheme not only improves network scalability but also ensures the perpetual operation of networks. Extensive simulation results demonstrate the effectiveness and efficiency of the proposed design. The results also validate the correctness of the theoretical analysis and show significant improvements that cut the number of nonfunctional nodes by half compared to the static scheme while maintaining the network overhead at the same level.

Mobile vehicles equipped with wireless energy transmission technology can recharge sensor nodes over the air. When to recharge which nodes, and in what order, critically impact the network performance. So far only a few works have studied the recharging policy for a single mobile vehicle. In this paper, we study how to coordinate the recharging activities of multiple mobile vehicles, which provide more scalability and robustness than a single vehicle. We leverage concepts and mechanisms from NDN (Named Data Networking) to design energy monitoring protocols that deliver energy status information to mobile vehicles in an efficient manner. Then we study how to minimize the total traveling cost of multiple vehicles while ensuring no node failure. We derive theoretical results on the energy neutral condition and the minimum number of mobile vehicles required for perpetual network operations. We formulate the optimization problem into a Multiple Traveling Salesman Problem with Deadlines (m-TSP with Deadlines), which is NP-hard. To accommodate the dynamic nature of node energy conditions and reduce computational overhead, we present a heuristic algorithm that selects the node with the minimum weighted sum of traveling time and residual lifetime. Our scheme not only improves network scalability but also guarantees the perpetual operation of networks. Finally, we conduct extensive simulations to demonstrate the effectiveness and efficiency of our proposed design, and validate the correctness of theoretical analysis.

A mobile vehicle equipped with wireless energy transmission technology can move around a wireless sensor network and recharge nodes over the air, leading to potentially perpetual operation if nodes can always be recharged before energy depletion. When to recharge which nodes, and in what order, critically impact the outcome. So far only a few works have studied this problem and relatively static recharging policies were proposed. However, dynamic changes such as unpredictable energy consumption variations in nodes, and practical issues like scalable and efficient gathering of energy information, are not yet addressed. In this paper, we propose NETWRAP, an NDN based Real Time Wireless Recharging Protocol for dynamic recharging in wireless sensor networks. We leverage concepts and mechanisms from NDN (Named Data Networking) to design a set of protocols that continuously gather and deliver energy information to the mobile vehicle, including unpredictable emergencies, in a scalable and efficient manner. We derive analytic results on energy neutral conditions that give rise to perpetual operation. We also discover that optimal recharging of multiple emergencies is an Orienteering problem with Knapsack approximation. Our extensive simulations demonstrate the effectiveness and efficiency of the proposed framework and validate the theoretical analysis.

In many mobile sensing applications devices need to discover new neighbors and maintain the rendezvous with known neighbors continuously. Due to the limited energy supply, these devices have to cycle their radios to conserve energy, making neighbor discovery and rendezvous maintenance even more challenging. To date, the main mechanism for device discover and rendezvous maintenance in existing solutions is pair wise, direct one-hop communication. We argue that such pair wise direct communication is sufficient but not necessary: there exist unnecessary active slots that can be eliminated, without affecting discovery and rendezvous. In this work, we propose a novel concept of extended quorum system, which leverages indirect discovery to further conserve energy. Specifically, we use quorum graph to capture all possible information flow paths where knowledge about known-neighbors can propagate among devices. By eliminating redundant paths, we can reduce the number of active slots significantly. Since a quorum graph can characterize arbitrary active schedules of mobile devices, our work can be broadly used to improve many existing quorum based discovery and rendezvous solutions. The simulation and test bed experimental results show that our solution can reduce as much as 55% energy consumption with a maximal 5% increase in latency for existing solutions.

Highly accurate indoor localization of smartphones is critical to enable novel location based features for users and businesses. In this paper, we first conduct an empirical investigation of the suitability of WiFi localization for this purpose. We find that although reasonable accuracy can be achieved, significant errors (e.g., $6\sim8m$) always exist. The root cause is the existence of distinct locations with similar signatures, which is a fundamental limit of pure WiFi-based methods. Inspired by high densities of smartphones in public spaces, we propose a peer assisted localization approach to eliminate such large errors. It obtains accurate acoustic ranging estimates among peer phones, then maps their locations jointly against WiFi signature map subjecting to ranging constraints. We devise techniques for fast acoustic ranging among multiple phones and build a prototype. Experiments show that it can reduce the maximum and 80-percentile errors to as small as $2m$ and $1m$, in time no longer than the original WiFi scanning, with negligible impact on battery lifetime.

As a supporting primitive of many mobile device applications, neighbor discovery identifies nearby devices so that they can exchange information and collaborate in a peer-to-peer manner. To date, discovery schemes trade a long latency for energy efficiency and require a collaborative duty cycle pattern, and thus they are not suitable for interactive mobile applications where a user is unable to configure others' devices. In this paper, we propose Acc, which serves as an on-demand generic discovery accelerating middleware for many existing neighbor discovery schemes. Acc leverages the discovery capabilities of neighbor devices, supporting both direct and indirect neighbor discoveries. Our evaluations show that Acc-assisted discovery schemes reduce latency by a maximum of 51.8%, compared with the schemes consuming the same amount of energy. We further present and evaluate a Crowd-Alert application where Acc can be employed by taxi drivers to accelerate selection of a direction with fewer competing taxis and more potential passengers, based on a 10 GB dataset of more than 15,000 taxis in a metropolitan area.

The rapid growth of sense-and-respond applications and the emerging cloud computing model present a new challenge: providing publish/subscribe as a scalable and elastic cloud service. This paper presents the Blue Dove attribute based publish/subscribe service that seeks to address such a challenge. Blue Dove uses a gossip-based one-hop overlay to organize servers into a scalable cluster. It proactively exploits skewness in data distribution to achieve high performance. By assigning each subscription to multiple servers through a multidimensional subscription space partitioning technique, it provides multiple candidate servers for each publication message. A message can be matched on any of its candidate servers with one hop forwarding. The performance-aware forwarding in Blue Dove ensures that the message is sent to the least loaded candidate server for processing, leading to low latency and high throughput. The evaluation shows that Blue Dove has a linear capacity increase as the system scales up, adapts to sudden workload changes within tens of seconds, and achieves multifold higher throughput than the techniques used in the existing enterprise and peer-to-peer pub/sub systems.

Quorum-based schemes, e.g., GQS, can ensure asynchronous neighbors to discover each other within bounded time under low-duty-cycle operations. But they are tightly based on the assumption that duty cycles of devices fit some specific prefixed patterns. We tackle this problem by proposing a distributed quorum system to speed up the discovery.

Queuing is commonly used to connect loosely coupled components to form large-scale, highly-distributed, and fault-tolerant applications. As cloud computing continues to gain popularity, a number of vendors have started offering cloud-hosted, multi-tenant queuing service. They provide high availability at the cost of reduced consistency. Although they offer at-least-once delivery guarantee, that is, no message loss, they do not make any effort in maintaining FIFO order, which is an important aspect of the queuing semantics. Thus they are not adequate for some applications. This paper presents the design and implementation of a scalable cloud-based queuing service, called Blue Dove Queuing Service (BDQS). It provides improved queuing consistency - at-least-once and best-effort in-order message delivery - while preserving high availability and reliability. It also offers clients a flexible trade-off between duplication and message order. Comprehensive evaluation is carried out on an Infrastructure-as-a-Service cloud computing platform with up to 70 server nodes and 1000 queues. It shows that BDQS achieves excellent performance scalability. Meanwhile, it offers an order-of-magnitude improvement in out-of-order measurement compared to existing no-order systems. Results also indicate that BDQS is highly reliable and available.

Stream processing is widely used by today's applications such as financial data analysis and disaster response. In distributed stream processing systems, machine fail-stop events are handled by either active standby or passive standby. However, existing high availability (HA) schemes have not sufficiently addressed the situation when a machine becomes temporarily unavailable due to data rate spikes, intensive analysis or job sharing, which happens frequently but lasts for short time. It is not clear how well active and passive standby fare against such transient unavailability. In this paper, we first critically examine the suitability of active and passive standby against transient unavailability in a real testbed environment. We find that both approaches have advantages and drawbacks, but neither is ideal to provide fast recovery at low overhead as required to handle transient unavailability. Based on the insights gained, we propose a novel hybrid HA method that switches between active and passive standby modes depending on the occurrence of failure events. It presents a desirable tradeoff that is different from existing HA approaches: low overhead during normal conditions and fast recovery upon transient or permanent failure events. We have implemented our hybrid method and compared it with existing HA designs with comprehensive evaluation. The results show that our hybrid method can reduce two-thirds of the recovery time compared to passive standby and 80% message overhead compared to active standby, allowing applications to enjoy uninterrupted processing without paying a high premium.

Federated systems have recently attracted much attention because they allow loosely coupled organizations to share resources for common benefits. However, discovering resources across administrative boundaries is challenging. Despite their willingness to share resources, many organizations prefer not to export their internal resource description to unfamiliar parties. While it is highly desirable to facilitate such voluntary sharing, the system also needs to resolve resource queries in an efficient manner. Unfortunately, none of the existing resource discovery designs, either hierarchical or DHT-based, can address these two challenges in the same time.In this paper, we present the design and evaluation of ROADS, a Replication Overlay Assisted resource Discovery Service for federated systems. In ROADS, the resource owners only export summaries, which are condensed representations of their resource records. These summaries are aggregated along a hierarchy and used to direct queries to appropriate resource owners. To improve its efficiency and resiliency, ROADS replicates the summaries using server overlays that enable "shortcuts'' in query forwarding. We have implemented ROADS and evaluated its performance through extensive analysis and experiments. The results show that ROADS outperforms a DHT-based design with 1-2 orders of magnitude less overhead in update messages and 50% less query forwarding time.

False data injection is a severe attack that compromised sensor nodes ("moles"1) can launch. These moles inject large amount of bogus traffic that can lead to application failures and exhausted network resources. Existing sensor network security proposals only passively mitigate the damage by filtering injected packets; they do not provide active means for fight back. This paper studies how to locate such moles within the framework of packet marking, when forwarding moles collude with source moles to manipulate the marks. Existing Internet traceback mechanisms do not assume compromised forwarding nodes and are easily defeated by manipulated marks. We propose a probabilistic nested marking (PNM) scheme that is secure against such colluding attacks. No matter how colluding moles manipulate the marks, PNM can always locate them one by one. We prove that nested marking is both sufficient and necessary to resist colluding attacks. PNM also has fast-traceback: within about 50 packets, it can track down a mole up to 20 hops away from the sink. This virtually prevents any effective data injection attack: moles will be caught before they have injected any meaningful amount of bogus traffic.

Intelligence Transportation Systems are critical to improve the efficiency of modern transportation. A system that is flexible and powerful enough to handle diverse demands from a large user base, is still elusive. Studies have shown that developing and integrating the various components constitute a significant portion of the capital cost and complexity of such systems. In this paper, we present a stream processing infrastructure we call System S. System S enables the deployment of large scale applications. It supports a mechanism for sharing data sources, software components, and even intermediate results allowing a reduction in the cost of software integration, and ownership. We experiment the stream processing infrastructure with a Fleet Management Center, and demonstrate how the infrastructure can be used to address unique issues in traffic management.

Node compromise poses severe security threats in wireless sensor networks. Unfortunately, existing security designs can address only a small, fixed threshold number of compromised nodes; the security protection completely breaks down when the threshold is exceeded. In this paper, we seek to overcome the threshold limitation and achieve resiliency against an increasing number of compromised nodes. To this end, we propose a novel location-based approach in which the secret keys are bound to geographic locations, and each node stores a few keys based on its own location. The location-binding property constrains the scope for which individual keys can be (mis)used, thus limiting the damages caused by a collection of compromised nodes. We illustrate this approach through the problem of report fabrication attacks, in which the compromised nodes forge non-existent events. We evaluate our design through extensive analysis, implementation and simulations, and demonstrate its graceful performance degradation in the presence of an increasing number of compromised nodes.

In a large-scale sensor network individual sensors are subject to security compromises. A compromised node can be used to inject bogus sensing reports. If undetected, these bogus reports would be forwarded to the data collection point (i.e., the sink). Such attacks by compromised nodes can result in not only false alarms but also the depletion of the finite amount of energy in a battery powered network. In this paper, we present a statistical en-route filtering (SEF) mechanism to detect and drop false reports during the forwarding process. Assuming that the same event can be detected by multiple sensors, in SEF each of the detecting sensors generates a keyed message authentication code (MAC) and multiple MACs are attached to the event report. As the report is forwarded, each node along the way verifies the correctness of the MAC's probabilistically and drops those with invalid MACs. SEF exploits the network scale to filter out false reports through collective decision-making by multiple detecting nodes and collective false detection by multiple forwarding nodes. We have evaluated SEF's feasibility and performance through analysis, simulation, and implementation. Our results show that SEF can be implemented efficiently in sensor nodes as small as Mica2. It can drop up to 70% of bogus reports injected by a compromised node within five hops, and reduce energy consumption by 65% or more in many cases.

In a large-scale sensor network individual sensors are subject to security compromises. A compromised node can inject into the network large quantities of bogus sensing reports which, if undetected, would be forwarded to the data collection point (i.e. the sink). Such attacks by compromised sensors can cause not only false alarms but also the depletion of the finite amount of energy in a battery powered network. We present a statistical en-route filtering (SEF) mechanism that can detect and drop such false reports. SEF requires that each sensing report be validated by multiple keyed message authentication codes (MACs), each generated by a node that detects the same event. As the report is forwarded, each node along the way verifies the correctness of the MACs probabilistically and drops those with invalid MACs at earliest points. The sink further filters out remaining false reports that escape the en-route filtering. SEF exploits the network scale to determine the truthfulness of each report through collective decision-making by multiple detecting nodes and collective false-report-detection by multiple forwarding nodes. Our analysis and simulations show that, with an overhead of 14 bytes per report, SEF is able to drop 80/spl sim/90% injected false reports by a compromised node within 10 forwarding hops, and reduce energy consumption by 50% or more in many cases.

In this paper we present PEAS, a robust energy-conserving protocol that can build long-lived, resilient sensor networks using a very large number of small sensors with short battery lifetime. PEAS extends the network lifetime by maintaining a necessary set of working nodes and turning off redundant ones. PEAS operations are based on individual node's observation of the local environment and do not require any node to maintain per neighbor node state. PEAS performance possesses a high degree of robustness in the presence of both node power depletions and unexpected failures. Our simulations and analysis show that PEAS can maintain an adequate working node density in the face of up to 38% node failures, and it can maintain roughly a constant overhead level under various deployment conditions ranging from sparse to very dense node deployment by using less than 1% of total energy consumption. As a result, PEAS can extend a sensor network's functioning time in linear proportion to the deployed sensor population.

No abstract available.

Small, inexpensive sensors with limited memory, computing power and short battery lifetimes are turning into reality. Due to adverse conditions such as high noise levels, extreme humidity or temperatures, or even destructions from unfriendly entities, sensor node failures may become norms rather than exceptions in real environments. To be practical, sensor networks must last for much longer times than that of individual nodes, and have yet to be robust against potentially frequent node failures. This paper presents the design of PEAS, a simple protocol that can build a long-lived sensor network and maintain robust operations using large quantities of economical, short-lived sensor nodes. PEAS extends system functioning time by keeping only a necessary set of sensors working and putting the rest into sleep mode. Sleeping ones wake up now and then, probing the local environment and replacing failed ones. The sleeping periods are self-adjusted dynamically, so as to keep the sensors' wakeup rate roughly constant, thus adapting to high node densities.

Sink mobility brings new challenges to large-scale sensor networking. It suggests that information about each mobile sink's location be continuously propagated through the sensor field to keep all sensor nodes updated with the direction of forwarding future data reports. Unfortunately frequent location updates from multiple sinks can lead to both excessive drain of sensors' limited battery power supply and increased collisions in wireless transmissions. In this paper we describe TTDD, a Two-Tier Data Dissemination approach that provides scalable and efficient data delivery to multiple mobile sinks. Each data source in TTDD proactively builds a grid structure which enables mobile sinks to continuously receive data on the move by flooding queries within a local cell only. TTDD's design exploits the fact that sensor nodes are stationary and location-aware to construct and maintain the grid structures with low overhead. We have evaluated TTDD performance through both analysis and extensive simulation experiments. Our results show that TTDD handles multiple mobile sinks efficiently with performance comparable with that of stationary sinks.

Wireless sensor networks offer a wide range of challenges to networking research, including unconstrained network scale, limited computing, memory and energy resources, and wireless channel errors. We study the problem of delivering messages from any sensor to an interested client user along the minimum-cost path in a large sensor network. We propose a new cost field based approach to minimum cost forwarding. In the design, we present a novel backoff-based cost field setup algorithm that finds the optimal costs of all nodes to the sink with one single message overhead at each node. Once the field is established, the message, carrying dynamic cost information, flows along the minimum cost path in the cost field. Each intermediate node forwards the message only if it finds itself to be on the optimal path, based on dynamic cost states. Our design does not require an intermediate node to maintain explicit "forwarding path" states. It requires a few simple operations and scales to any network size. We show the correctness and effectiveness of the design by both simulations and analysis.